ModelRoute: Cost-Aware API Routing and Multi-Model Optimization Proxy

We've standardized on Claude for the heavy lifting, reasoning tasks, content generation, anything that needs to not sound robotic. GPT-4 for some of the faster, more straightforward API calls where latency matters more than depth. Anthropic's batch API helps with cost when we're processing user data overnight. The real differentiator isn't which model you pick, it's how tightly you integrate it into your actual product flow. Most teams I know waste cycles trying to make a single model do everything. We ended up asking: where does the user actually feel the intelligence, and what are we willing to spend there? Then everything else gets the cheaper, faster option. If you're just starting, Claude or GPT-4 will both work fine. The switching cost is low enough that you can iterate on your product while you figure out which one feels right for your specific use case.

Depends entirely on which part of the stack, and I'd argue the interesting cost story isn't in the text models at all. For text on our product (socialshot.ai): Claude for anything user facing where tone matters, cheaper open weight models for the mechanical stuff, classification, extraction, structured rewriting. That split saves far more than swapping the flagship, because most calls in a real product are boring and don't need the good model. Route by task, not by vendor loyalty. For image and video generation, which is the bulk of our spend, the cost differences between providers are enormous and the pricing is much less transparent than text. Two things I learned the expensive way there: Read the invoice, not the docs. A model we assumed billed per second of video actually bills by pixel area. Our 1080p tier was running at a loss for weeks before I checked the billing dashboard rather than the pricing page. Also worth checking: does the provider charge for failed generations? Ours does. Cheaper variants of the same model family are usually the real win, not a different vendor. Moving one expensive path to a "mini" version of the same model roughly halved that cost with no complaint from users, which was a bigger saving than any provider switch we tested. On the Chinese models specifically: for image and video they're genuinely competitive and in some cases better on price to quality. For text in a customer facing product, the thing that keeps us on Claude isn't raw capability, it's consistency across edge cases and not having to re-tune prompts every time a model updates. That stability has a real dollar value that doesn't show up in the per token comparison.

gemini flash for the high volume path, its cheap enough to run a lot of queries per user without thinking about it. claude and gpt for anything where the output quality actually matters more than cost. perplexity for anything that needs live web grounding. the thing that took me longest to internalise was that model choice is mostly a cost architecture decision, not a quality one. once i split the cheap path from the expensive path the bill stopped being scary.